Benzodiazepines (BDZs) and non-BDZ sedative-hypnotics are effective for the management of chronic insomnia; however, they are associated with adverse effects such as headache, dizziness, and palpitations. Furthermore, long-term use of these medications is associated with decreased blood pressure (BP) or depressed baroreflex function. Therefore, here, we assessed whether BDZs and non-BDZs cause vasorelaxation directly. Vasorelaxation in response to 22 BDZs, 2 non-BDZs, and tandospirone was determined by myograph methods using isolated Wistar rat thoracic aortas. All the drugs relaxed phenylephrine-contracted rat aortas in a concentration-dependent manner. Zolpidem and tandospirone caused over 80% relaxation at a concentration of 10 μM; diazepam, estazolam, etizolam, and tofisopam caused 60-70% relaxation; whereas 18 other BDZs (alprazolam, bromazepam, brotizolam, chlordiazepoxide, clobazam, clonazepam, clorazepate, ethyl loflazepate, flunitrazepam, flurazepam, lorazepam, lormetazepam, midazolam, nimetazepam, nitrazepam, oxazepam, temazepam, and triazolam) and zaleplon caused less than 50% relaxation. The relaxation was partially but significantly inhibited to the same extent by a nitric oxide (NO) synthase antagonist and after endothelium removal. Binding assay of gamma-aminobutyric acid type A receptors was performed using [3H]flunitrazepam. No correlation was observed between vasorelaxation at a concentration of 10 μM and the binding affinities for 23 drugs. The study demonstrated that zaleplon, zolpidem, tandospirone, and many BDZs cause vasorelaxation to different extents via endothelial NO-dependent and endothelium-independent pathways. In conclusion, the direct vasodilatory effects of these drugs may be involved in the mechanisms underlying their adverse effects. Additionally, the decreased BP observed in persons who take BDZs or non-BDZs may be partly due to direct vasodilation.Previous studies showed that cannabinoid 1 receptor (CB1) is linked with skin fibrosis and scar tissue formation in mice. Therefore, the topical use of cannabinoids may have a role in the prevention or treatment of local fibrotic and wound healing diseases as hypertrophic scars or keloids. In this study, we asked whether CB1 activation or inactivation would change fibroblast differentiation into myofibroblast and collagen deposition in skin human fibroblast. Primary cultures of adult human fibroblasts were obtained from abdominal human skin. Cells were stimulated with transforming growth factor-beta (TGF-β, 10ng/ml) and treated with a CB1 selective agonist (arachidonyl-2-chloroethylamide, ACEA 1 μM) and an antagonist (AM251 1, 5 and 10 μM). Alpha-smooth muscle actin (α-SMA) was quantified using Immunocytochemistry and Western Blot. Collagen was quantified with Sirius Red staining assay. Significance was assessed by One-way ANOVA. P less then 0.05 was considered signi?cant. TGF-β significantly increases α-SMA expression. ACEA 1 μM significantly increases collagen deposition but does not change α-SMA expression. AM251 10 μM added in the absence and the presence of ACEA reduces α-SMA expression and collagen content in TGF-β treated cells. AM251 shows a concentration-dependent effect over collagen deposition with a pIC50 of 5.5 (4.6-6.4). TGF-β significantly increases CB1 receptor expression. CB1 inactivation with AM251 prevents fibroblasts differentiation and collagen deposition, induced by TGF-β in human fibroblasts. The outcome supports that CB1 is a molecular target for wound healing disorders and in vivo and pre-clinical studies should be implemented to clarify this premise.Copeptin, a glycosylated peptide fragment derived from the C-terminal region of the precursor of arginine8 vasopressin (AVP), is co-secreted with AVP in equimolar amounts. Elevated plasma AVP modulates gastric motility so we investigated whether copeptin had a similar effect. Copeptin (10-9-10-7M), and AVP (10-12-10-5M), were evaluated for their ability to modulate spontaneous and electrically-evoked (EFS) contractions of human proximal and distal gastric circular muscle in vitro. Similar experiments were performed on the mouse stomach and we re-examined the published effect of copeptin on the mouse aorta. In the presence of tetrodotoxin (10-6M), atropine (10-6M) and L-NAME (3 × 10-4M), human proximal and distal stomach muscle contracted spontaneously and rhythmically as did mouse distal stomach. Copeptin (10-9-10-7M), had no effect on baseline muscle tone or myogenic spontaneous contractions of either human or mouse stomach. However, AVP concentration-dependently increased tone, amplitude and frequency of contractions in both regions of human stomach with similar potency (pEC50 9.0-9.5; n = 4) and threshold concentration (10-11-10-10M). https://www.selleckchem.com/products/nlg919.html AVP was similarly active in the mouse stomach. EFS-evoked cholinergic contractions (human and mouse) were unaffected by both peptides EFS-evoked relaxations of mouse stomach were unaffected by copeptin. In sub-maximally contracted mouse aorta the elevated tone was unaffected by copeptin (10-7M) (cf. previously published study) but was reduced by carbachol (10-6M) and sodium nitroprusside (10-3M). We conclude that in contrast to AVP, copeptin over a concentration range reported in the plasma has no direct ability to modulate the motility of the human and mouse stomach.The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales - from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality. To do so, they must be systems-level models that incorporate interactions between measurable aspects of age-associated changes. To incorporate individual variability in the aging process, models must be stochastic. To be useful they should also be predictive, and so must be fit or parameterized by data from large populations of aging individuals. In this perspective, we outline where we have been, where we are, and where we hope to go with such computational models of aging. Our focus is on data-driven systems-level models, and on their great potential in aging research.